Errata Updates to “Principles of Business Forecasting” By Ord and Fildes
After Chapters 3 and 4 were completed, the Exponential Smoothing Macro [ESM] was
developed. The ESM is more comprehensive and more flexible than the macros developed
earlier and cited in the text [SES.xlsm, LES.xlsm, HW_quarterly.xlsm and HW_monthly.xlsm].
Thus, wherever one of these earlier macros is mentioned, the reader should use the ESM instead.
The ESM is described in detail in the website materials; Appendices 3A and 4A may be ignored.
NOTE: The solutions on the website are not in general meant to be a complete answer to the
question posed (particularly for the more complex cases). Some of the solutions have not yet
been posted. If you have queries or improvements to suggest, please contact one of the authors.
OTHER CHANGES
Page 7, section 1.3.1. Definitions for Trend and Seasonal should be provided at this stage. For
consistency with later discussions (pages 74 and 100 respectively) we recommend the following:
Trend: A time series is said to have a trend if the mean level at any point in time is expected to
change, relative to past values. Such trends may be linear, exponential or evolve in a local
fashion.
Seasonal: A time series is said to have a seasonal component if it displays a recurrent pattern
with a fixed and known duration (e.g. by days of the week or months of the year)
In presenting the introductory material in chapters 1 and 2 we found the following amended and
new slides useful
Examples of Forecasting Problems
Components of the (retail) time series:
• Trend: A systematic change (up or down) in the mean level of a time
series.
• Seasonal: A time series is said to have a seasonal component if it displays
a recurrent pattern with a fixed and known duration
(Ord & Fildes, Glossary)
Ord/Fildes Principles of Business Forecasting 1e Errata Sheet • September 2014
Types of Data
COMPARABLE DATA
Data observed over time are not necessarily
comparable to each other because:

The time periods are of differing lengths

The units they are measured in change

The definitions of what is being measured change

They are incorrectly measured
- data errors arise from sampling, from bias in the instruments or
the responses, from transcription.
We need to understand how the data are measured, their
weaknesses and biases
Page 59: We now provide a single Excel macro, the Exponential Smoothing Macro (ESM) along
with a complete guide, ESM Manual, on the book’s website. All later references to exponential
smoothing macros should be taken to refer to the ESM; see, for example, the first sentence in
section 3.3.2 on page 69, the footnote on page 73 and the end-of-chapter Exercises for Chapters
3 and 4.
Page 70, Table 3.4: The entries in columns SES (0.5) and SES (opt) are incorrect. Delete the first
entry in each column and move the remaining entries up by one row. In the last row (period 36)
add the entries 32409 and 31739 respectively. Figure 3.7 is similarly affected. The corrected
table and figure are provided below.
Table 3.4: Actual and one-step-ahead forecasts for WFJ Sales for periods 27–36, starting from
period 26 as origin and using SES.xlsm
Period
WFJ
Sales
SES
(0.2)
SES
(0.5)
SES
(opt)
27
30,986
33,321
34,003
35,417
33,822
32,723
34,925
33,460
30,999
31,286
33,884
33,304
33,308
33,447
33,841
33,837
33,614
33,876
33,793
33,234
34,346
32,666
32,993
33,498
34,458
34,140
33,432
34,178
33,819
32409
34,580
31,963
32,952
33,717
34,955
34,130
33,105
34,431
33,723
31739
28
29
30
31
32
33
34
35
36
Ord/Fildes Principles of Business Forecasting 1e Errata Sheet • September 2014
Figure 3.7: SES FORECASTS FOR WFJ SALES: THE EFFECTS
OF DIFFERENT SMOOTHING CONSTANTS
36,000
35,500
35,000
34,500
34,000
33,500
33,000
32,500
32,000
31,500
31,000
30,500
27
28
29
WFJ Sales
30
31
32
SES (0.2)
33
SES (0.5)
34
35
36
SES (opt)
Page 73, footnote 5: The cross-reference should be to Chapter 9, section 9.6.1.
Page 88: The ESM allows values of c in the range 0  c  1 with c=0 defaulting to the log
transform.
p. 93: Exercise 3.7 should read 1989, not 1990
p. 93: Exercise 3.0 should read Netflix_1 not Netflix
Page 94, Minicase 3.2: The cross-reference in the first line should be to Minicase 2.2, not Table
2.11.
p. 95: Table 3.20 uses Netflix.xlsx not Netflix_2.xlsx
Page 97, Appendix 3A: Ignore this material and use the ESM Guide, available on the book
website.
p. 104: The file SES_12_months is now included in Data Sets
p. 114: Table 4.5B is now included in Examples_chapter_4.xlsx
p. 122: The file Exercise_4_2.xlsx is now included in Data Sets
Page 126, Appendix 4A: Ignore this material and use the ESM Guide, available on the book
website.
Pages 143-144: The cross-references to earlier exercises in Exercises 5.2 and 5.3 should be to
Exercises 3.1, 3.3 and 3.2, 3.5 respectively and not to items in Chapter 4.
p. 144: Exercise 5.9 should refer back to Minicase 4.1, not 5.1
Ord/Fildes Principles of Business Forecasting 1e Errata Sheet • September 2014
Page 144, Exercise 5.9: The cross-references should be to Minicase 2.2 (not Table 2.11) and
Minicase 4.1 (not 5.1).
In chapter 8 the residuals slide should be amended as follows:
8.5: Checking the Assumptions
Key assumptions and how to check them
Model for Y is linear Plot errors against fitted Y: look for non-linearity.
in X
Also plot errors against new X variables, if
available.
Errors have mean
Cannot be tested directly; check the
zero
measurement process for bias.
Errors have equal
variances
Plot the residuals against fitted values: look for
increasing / decreasing scatter
Errors are
independent
 Of each other
 Of Xs
Normality
Plot the residuals against time order: look for
pattern. Examine the ACF of the residuals.
Plot errors vs X and Predictions
‘Reset test’
Examine the histogram and the normal
probability plot.
After fitting the model, look for unusual values in
the plots of the residuals
Outliers
The outline solution to mini-case 8.5 has been extended.
There is a minor correction to slide 9.5.2 as follows:
Ord/Fildes Principles of Business Forecasting 1e Errata Sheet • September 2014